From plant biology research to technology transfer and knowledge extension: improving food quality and mitigating environmental impacts
Bibliographic record
Abstract
Academic scientists face an unpredictable path from plant biology research to real-life application. Fundamental studies of γ-aminobutyrate and carotenoid metabolism, control of Botrytis infection, and the uptake and distribution of mineral nutrients illustrate that most academic research in plant biology could lead to innovative solutions for food, agriculture, and the environment. The time to application depends on various factors such as the fundamental nature of the scientific questions, the development of enabling technologies, the research priorities of funding agencies, the existence of competitive research, the willingness of researchers to become engaged in commercial activities, and ultimately the insight and creativity of the researchers. Applied research is likely to be adopted more rapidly by industry than basic research, so academic scientists engaged in basic research are less likely to participate in science commercialization. It is argued that the merit of Discovery Grant applications to the Natural Sciences and Engineering Research Council (NSERC) of Canada should not be evaluated for their potential impact on policy and (or) technology. Matching industry funds in Canada rarely support the search for knowledge. Therefore, NSERC Discovery Grants should fund basic research in its entirety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".